{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-06-26T07:42:06.803334Z",
     "start_time": "2024-06-26T07:42:06.684997Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "df = pd.read_csv('./Women_s_E-Commerce_Clothing_Reviews_1594_1.csv', sep=';', on_bad_lines='skip')\n",
    "df.head(10)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Unnamed: 0  Clothing.ID  Age                                 Title  \\\n",
       "0           1          767   33                                   NaN   \n",
       "1           2         1080   34                                   NaN   \n",
       "2           3         1077   60               Some major design flaws   \n",
       "3           4         1049   50                      My favorite buy!   \n",
       "4           5          847   47                      Flattering shirt   \n",
       "5           6         1080   49               Not for the very petite   \n",
       "6           7          858   39                  Cagrcoal shimmer fun   \n",
       "7           8          858   39  Shimmer, surprisingly goes with lots   \n",
       "8           9         1077   24                            Flattering   \n",
       "9          10         1077   34                     Such a fun dress!   \n",
       "\n",
       "                                         Review.Text  Rating  Recommended.IND  \\\n",
       "0  Absolutely wonderful - silky and sexy and comf...       4                1   \n",
       "1  Love this dress!  it's sooo pretty.  i happene...       5                1   \n",
       "2  I had such high hopes for this dress and reall...       3                0   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5                1   \n",
       "4  This shirt is very flattering to all due to th...       5                1   \n",
       "5  I love tracy reese dresses, but this one is no...       2                0   \n",
       "6  I aded this in my basket at hte last mintue to...       5                1   \n",
       "7  I ordered this in carbon for store pick up, an...       4                1   \n",
       "8  I love this dress. i usually get an xs but it ...       5                1   \n",
       "9  I'm 5\"5' and 125 lbs. i ordered the s petite t...       5                1   \n",
       "\n",
       "   Positive.Feedback.Count   Division.Name Department.Name Class.Name  \n",
       "0                        0       Initmates        Intimate  Intimates  \n",
       "1                        4         General         Dresses    Dresses  \n",
       "2                        0         General         Dresses    Dresses  \n",
       "3                        0  General Petite         Bottoms      Pants  \n",
       "4                        6         General            Tops    Blouses  \n",
       "5                        4         General         Dresses    Dresses  \n",
       "6                        1  General Petite            Tops      Knits  \n",
       "7                        4  General Petite            Tops      Knits  \n",
       "8                        0         General         Dresses    Dresses  \n",
       "9                        0         General         Dresses    Dresses  "
      ],
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       "      <td>My favorite buy!</td>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>5</td>\n",
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       "      <td>I love tracy reese dresses, but this one is no...</td>\n",
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       "      <td>7</td>\n",
       "      <td>858</td>\n",
       "      <td>39</td>\n",
       "      <td>Cagrcoal shimmer fun</td>\n",
       "      <td>I aded this in my basket at hte last mintue to...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>Knits</td>\n",
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       "      <th>7</th>\n",
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       "      <td>Shimmer, surprisingly goes with lots</td>\n",
       "      <td>I ordered this in carbon for store pick up, an...</td>\n",
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       "      <td>General Petite</td>\n",
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       "      <td>1077</td>\n",
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       "      <td>I love this dress. i usually get an xs but it ...</td>\n",
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       "      <td>Such a fun dress!</td>\n",
       "      <td>I'm 5\"5' and 125 lbs. i ordered the s petite t...</td>\n",
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     "execution_count": 18,
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   "execution_count": 18
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     "start_time": "2024-06-26T07:42:06.803334Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df.drop('Unnamed: 0', axis=1, inplace=True)\n",
    "df.head(10)"
   ],
   "id": "2e3d5160d256408a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Clothing.ID  Age                                 Title  \\\n",
       "0          767   33                                   NaN   \n",
       "1         1080   34                                   NaN   \n",
       "2         1077   60               Some major design flaws   \n",
       "3         1049   50                      My favorite buy!   \n",
       "4          847   47                      Flattering shirt   \n",
       "5         1080   49               Not for the very petite   \n",
       "6          858   39                  Cagrcoal shimmer fun   \n",
       "7          858   39  Shimmer, surprisingly goes with lots   \n",
       "8         1077   24                            Flattering   \n",
       "9         1077   34                     Such a fun dress!   \n",
       "\n",
       "                                         Review.Text  Rating  Recommended.IND  \\\n",
       "0  Absolutely wonderful - silky and sexy and comf...       4                1   \n",
       "1  Love this dress!  it's sooo pretty.  i happene...       5                1   \n",
       "2  I had such high hopes for this dress and reall...       3                0   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5                1   \n",
       "4  This shirt is very flattering to all due to th...       5                1   \n",
       "5  I love tracy reese dresses, but this one is no...       2                0   \n",
       "6  I aded this in my basket at hte last mintue to...       5                1   \n",
       "7  I ordered this in carbon for store pick up, an...       4                1   \n",
       "8  I love this dress. i usually get an xs but it ...       5                1   \n",
       "9  I'm 5\"5' and 125 lbs. i ordered the s petite t...       5                1   \n",
       "\n",
       "   Positive.Feedback.Count   Division.Name Department.Name Class.Name  \n",
       "0                        0       Initmates        Intimate  Intimates  \n",
       "1                        4         General         Dresses    Dresses  \n",
       "2                        0         General         Dresses    Dresses  \n",
       "3                        0  General Petite         Bottoms      Pants  \n",
       "4                        6         General            Tops    Blouses  \n",
       "5                        4         General         Dresses    Dresses  \n",
       "6                        1  General Petite            Tops      Knits  \n",
       "7                        4  General Petite            Tops      Knits  \n",
       "8                        0         General         Dresses    Dresses  \n",
       "9                        0         General         Dresses    Dresses  "
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       "      <td>I aded this in my basket at hte last mintue to...</td>\n",
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       "      <td>Shimmer, surprisingly goes with lots</td>\n",
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       "      <td>I love this dress. i usually get an xs but it ...</td>\n",
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       "      <td>Such a fun dress!</td>\n",
       "      <td>I'm 5\"5' and 125 lbs. i ordered the s petite t...</td>\n",
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      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
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   },
   "cell_type": "code",
   "source": "df.info()",
   "id": "d564accc7e1506c7",
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    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23486 entries, 0 to 23485\n",
      "Data columns (total 10 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   Clothing.ID              23486 non-null  int64 \n",
      " 1   Age                      23486 non-null  int64 \n",
      " 2   Title                    19676 non-null  object\n",
      " 3   Review.Text              22641 non-null  object\n",
      " 4   Rating                   23486 non-null  int64 \n",
      " 5   Recommended.IND          23486 non-null  int64 \n",
      " 6   Positive.Feedback.Count  23486 non-null  int64 \n",
      " 7   Division.Name            23472 non-null  object\n",
      " 8   Department.Name          23472 non-null  object\n",
      " 9   Class.Name               23472 non-null  object\n",
      "dtypes: int64(5), object(5)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "8075eecef57ea86b"
  },
  {
   "metadata": {
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     "end_time": "2024-06-26T07:42:06.865390Z",
     "start_time": "2024-06-26T07:42:06.837839Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df.dropna(subset=['Review.Text', 'Title', 'Rating'], inplace=True)\n",
    "df.info()"
   ],
   "id": "8b6637313728efc9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19675 entries, 2 to 23485\n",
      "Data columns (total 10 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   Clothing.ID              19675 non-null  int64 \n",
      " 1   Age                      19675 non-null  int64 \n",
      " 2   Title                    19675 non-null  object\n",
      " 3   Review.Text              19675 non-null  object\n",
      " 4   Rating                   19675 non-null  int64 \n",
      " 5   Recommended.IND          19675 non-null  int64 \n",
      " 6   Positive.Feedback.Count  19675 non-null  int64 \n",
      " 7   Division.Name            19662 non-null  object\n",
      " 8   Department.Name          19662 non-null  object\n",
      " 9   Class.Name               19662 non-null  object\n",
      "dtypes: int64(5), object(5)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-26T07:42:06.873185Z",
     "start_time": "2024-06-26T07:42:06.865900Z"
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   },
   "cell_type": "code",
   "source": "df.head()",
   "id": "aee80a22f63ee8a0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Clothing.ID  Age                    Title  \\\n",
       "2         1077   60  Some major design flaws   \n",
       "3         1049   50         My favorite buy!   \n",
       "4          847   47         Flattering shirt   \n",
       "5         1080   49  Not for the very petite   \n",
       "6          858   39     Cagrcoal shimmer fun   \n",
       "\n",
       "                                         Review.Text  Rating  Recommended.IND  \\\n",
       "2  I had such high hopes for this dress and reall...       3                0   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5                1   \n",
       "4  This shirt is very flattering to all due to th...       5                1   \n",
       "5  I love tracy reese dresses, but this one is no...       2                0   \n",
       "6  I aded this in my basket at hte last mintue to...       5                1   \n",
       "\n",
       "   Positive.Feedback.Count   Division.Name Department.Name Class.Name  \n",
       "2                        0         General         Dresses    Dresses  \n",
       "3                        0  General Petite         Bottoms      Pants  \n",
       "4                        6         General            Tops    Blouses  \n",
       "5                        4         General         Dresses    Dresses  \n",
       "6                        1  General Petite            Tops      Knits  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clothing.ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Review.Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended.IND</th>\n",
       "      <th>Positive.Feedback.Count</th>\n",
       "      <th>Division.Name</th>\n",
       "      <th>Department.Name</th>\n",
       "      <th>Class.Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077</td>\n",
       "      <td>60</td>\n",
       "      <td>Some major design flaws</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1049</td>\n",
       "      <td>50</td>\n",
       "      <td>My favorite buy!</td>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>847</td>\n",
       "      <td>47</td>\n",
       "      <td>Flattering shirt</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1080</td>\n",
       "      <td>49</td>\n",
       "      <td>Not for the very petite</td>\n",
       "      <td>I love tracy reese dresses, but this one is no...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>858</td>\n",
       "      <td>39</td>\n",
       "      <td>Cagrcoal shimmer fun</td>\n",
       "      <td>I aded this in my basket at hte last mintue to...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Knits</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-26T07:42:06.881081Z",
     "start_time": "2024-06-26T07:42:06.874190Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df['Rating'].value_counts()\n",
    "df['评价'] = pd.cut(df['Rating'], bins=[0, 2, 4, 5], labels=['差评', '中评', '好评'])\n",
    "df['评价'].value_counts()\n"
   ],
   "id": "923347ede5233874",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "好评    10871\n",
       "中评     6753\n",
       "差评     2051\n",
       "Name: 评价, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-26T07:42:06.979877Z",
     "start_time": "2024-06-26T07:42:06.882414Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "sns.countplot(x='评价', data=df)"
   ],
   "id": "9f5ec62d7224be2a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='评价', ylabel='count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-26T07:42:10.069527Z",
     "start_time": "2024-06-26T07:42:06.981402Z"
    }
   },
   "cell_type": "code",
   "source": [
    "g=sns.FacetGrid(data=df, col='Class.Name', col_wrap=3,sharex=False, sharey=False)\n",
    "g.map(sns.countplot, '评价', order=['差评', '中评', '好评'])"
   ],
   "id": "9eada9cf1b464be4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x24bb80c51d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 900x2100 with 20 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-26T07:42:10.078538Z",
     "start_time": "2024-06-26T07:42:10.070530Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from nltk.corpus import wordnet as wn\n",
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "df['Class.Name'].unique()\n",
    "df_positive=df[df['评价']=='好评']\n",
    "df_negative=df[df['评价']=='差评']\n",
    "df_neutral=df[df['评价']=='中评']\n",
    "\n"
   ],
   "id": "8f59727c04625333",
   "outputs": [],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-26T07:42:17.392746Z",
     "start_time": "2024-06-26T07:42:10.078538Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#标点符号\n",
    "punctuation = [\",\", \":\",\"''\",\";\", \".\", \"!\", \"'\", '\"', \"’\", \"?\", \"/\", \"-\", \"+\", \"&\", \"(\", \")\",\"The\",\"n't\",\"could\"]\n",
    "# 从nltk的安装目录下加载停用词表\n",
    "stop_words = nltk.corpus.stopwords.words('english')+punctuation\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "def get_lemma(word):\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    # 注意：wordnet需要一个词性和单词作为输入，此处默认为名词'noun'\n",
    "    return lemmatizer.lemmatize(word, pos='n')\n",
    "\n",
    "import re\n",
    "def prepare_text(sentence):\n",
    "    # 先去除标点符号\n",
    "    sentence = re.sub(r'[^\\w\\s]', '', sentence)\n",
    "    # 传入一句话, 先走英文分词\n",
    "    tokens = nltk.word_tokenize(sentence)\n",
    "    # 走词形还原\n",
    "    tokens = [get_lemma(token) for token in tokens]\n",
    "    # 去停用词\n",
    "    tokens = [i for i in tokens if i.lower() not in stop_words]\n",
    "    return tokens\n",
    "\n",
    "# 接下来的代码保持不变\n",
    "\n",
    "df_positive['关键词'] = df_positive['Review.Text'].apply(prepare_text)\n",
    "df_negative['关键词'] = df_negative['Review.Text'].apply(prepare_text)\n",
    "df_neutral['关键词'] = df_neutral['Review.Text'].apply(prepare_text)\n",
    "df_positive.head()"
   ],
   "id": "95d064acb814fe28",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\iosluna\\AppData\\Local\\Temp\\ipykernel_122740\\1650956675.py:25: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_positive['关键词'] = df_positive['Review.Text'].apply(prepare_text)\n",
      "C:\\Users\\iosluna\\AppData\\Local\\Temp\\ipykernel_122740\\1650956675.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_negative['关键词'] = df_negative['Review.Text'].apply(prepare_text)\n",
      "C:\\Users\\iosluna\\AppData\\Local\\Temp\\ipykernel_122740\\1650956675.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_neutral['关键词'] = df_neutral['Review.Text'].apply(prepare_text)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   Clothing.ID  Age                 Title  \\\n",
       "3         1049   50      My favorite buy!   \n",
       "4          847   47      Flattering shirt   \n",
       "6          858   39  Cagrcoal shimmer fun   \n",
       "8         1077   24            Flattering   \n",
       "9         1077   34     Such a fun dress!   \n",
       "\n",
       "                                         Review.Text  Rating  Recommended.IND  \\\n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5                1   \n",
       "4  This shirt is very flattering to all due to th...       5                1   \n",
       "6  I aded this in my basket at hte last mintue to...       5                1   \n",
       "8  I love this dress. i usually get an xs but it ...       5                1   \n",
       "9  I'm 5\"5' and 125 lbs. i ordered the s petite t...       5                1   \n",
       "\n",
       "   Positive.Feedback.Count   Division.Name Department.Name Class.Name  评价  \\\n",
       "3                        0  General Petite         Bottoms      Pants  好评   \n",
       "4                        6         General            Tops    Blouses  好评   \n",
       "6                        1  General Petite            Tops      Knits  好评   \n",
       "8                        0         General         Dresses    Dresses  好评   \n",
       "9                        0         General         Dresses    Dresses  好评   \n",
       "\n",
       "                                                 关键词  \n",
       "3  [love, love, love, jumpsuit, fun, flirty, fabu...  \n",
       "4  [shirt, flattering, due, adjustable, front, ti...  \n",
       "6  [aded, basket, hte, last, mintue, see, would, ...  \n",
       "8  [love, dress, usually, get, x, run, little, sn...  \n",
       "9  [Im, 55, 125, lb, ordered, petite, make, sure,...  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clothing.ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Review.Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended.IND</th>\n",
       "      <th>Positive.Feedback.Count</th>\n",
       "      <th>Division.Name</th>\n",
       "      <th>Department.Name</th>\n",
       "      <th>Class.Name</th>\n",
       "      <th>评价</th>\n",
       "      <th>关键词</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1049</td>\n",
       "      <td>50</td>\n",
       "      <td>My favorite buy!</td>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>Pants</td>\n",
       "      <td>好评</td>\n",
       "      <td>[love, love, love, jumpsuit, fun, flirty, fabu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>847</td>\n",
       "      <td>47</td>\n",
       "      <td>Flattering shirt</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Blouses</td>\n",
       "      <td>好评</td>\n",
       "      <td>[shirt, flattering, due, adjustable, front, ti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>858</td>\n",
       "      <td>39</td>\n",
       "      <td>Cagrcoal shimmer fun</td>\n",
       "      <td>I aded this in my basket at hte last mintue to...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Knits</td>\n",
       "      <td>好评</td>\n",
       "      <td>[aded, basket, hte, last, mintue, see, would, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1077</td>\n",
       "      <td>24</td>\n",
       "      <td>Flattering</td>\n",
       "      <td>I love this dress. i usually get an xs but it ...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>好评</td>\n",
       "      <td>[love, dress, usually, get, x, run, little, sn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1077</td>\n",
       "      <td>34</td>\n",
       "      <td>Such a fun dress!</td>\n",
       "      <td>I'm 5\"5' and 125 lbs. i ordered the s petite t...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>好评</td>\n",
       "      <td>[Im, 55, 125, lb, ordered, petite, make, sure,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-26T07:42:17.509895Z",
     "start_time": "2024-06-26T07:42:17.392746Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from collections import Counter\n",
    "positive=df_positive['关键词'].to_list()\n",
    "negative=df_negative['关键词'].to_list()\n",
    "neutral=df_neutral['关键词'].to_list()\n",
    "def get_words(text):\n",
    "    words_all = []\n",
    "    # 从 二维列表中, 遍历所有的单词, 放到同一个一维列表中\n",
    "    # text 是所有的好评/差评\n",
    "    for words in text:\n",
    "        for word in words:\n",
    "            words_all.append(word)\n",
    "    # 通过Counter来统计词频\n",
    "    word_freq = Counter(words_all)\n",
    "    # 所有的文本进行去重\n",
    "    total_words = list(set(words_all))\n",
    "    #平均每条评论有多少词 len(words_all) 这些评论中一共有多少个词  len(text) 一共有多少条评论\n",
    "    content_mean = len(words_all)/len(text)\n",
    "    # 去重之后的文本数量/没有去重的文本数量\n",
    "    words_cap =len(total_words)/len(words_all)\n",
    "    return word_freq,content_mean,words_cap\n",
    "word_freq_positive,content_mean_positive,words_cap_positive = get_words(positive)\n",
    "positive_words_wordcloud = word_freq_positive.most_common(100)\n",
    "word_freq_negative,content_mean_negative,words_cap_negative = get_words(negative)\n",
    "negative_words_wordcloud = word_freq_negative.most_common(100)\n",
    "word_freq_neutral,content_mean_neutral,words_cap_neutral = get_words(neutral)\n",
    "neutral_words_wordcloud = word_freq_neutral.most_common(100)"
   ],
   "id": "12a92d91d227f727",
   "outputs": [],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-26T07:42:17.522167Z",
     "start_time": "2024-06-26T07:42:17.509895Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from pyecharts.charts import WordCloud\n",
    "import pyecharts.options as opts\n",
    "(WordCloud().add(series_name=\"好评词云\",\n",
    "                 data_pair=positive_words_wordcloud, #传入绘制词云图的数据\n",
    "                 word_size_range=[16, 80]) #word_size_range字号大小取值范围\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(\n",
    "        title=\"好评词云\", \n",
    "        title_textstyle_opts=opts.TextStyleOpts(font_size=23) # 设置标题字号\n",
    " ),\n",
    "tooltip_opts=opts.TooltipOpts(is_show=True),# 设置为True 鼠标滑过文字会弹出提示框\n",
    "            ).\n",
    " render('positive_words.html')\n",
    ")"
   ],
   "id": "980fbd9af1e56b94",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\数据分析\\\\day08\\\\02_代码\\\\positive_words.html'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-26T07:42:17.535628Z",
     "start_time": "2024-06-26T07:42:17.523181Z"
    }
   },
   "cell_type": "code",
   "source": [
    "(WordCloud().add(series_name=\"中评文本\",\n",
    "                 data_pair=neutral_words_wordcloud, #传入绘制词云图的数据\n",
    "                 word_size_range=[16, 80]) #word_size_range字号大小取值范围\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(\n",
    "        title=\"中评词云图\", \n",
    "        title_textstyle_opts=opts.TextStyleOpts(font_size=23) # 设置标题字号\n",
    " ),\n",
    "tooltip_opts=opts.TooltipOpts(is_show=True),# 设置为True 鼠标滑过文字会弹出提示框\n",
    "            ).\n",
    " render('neutral_words.html')\n",
    ")"
   ],
   "id": "1bda958a68dbf85a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\数据分析\\\\day08\\\\02_代码\\\\neutral_words.html'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-26T07:42:17.544709Z",
     "start_time": "2024-06-26T07:42:17.536140Z"
    }
   },
   "cell_type": "code",
   "source": [
    "(WordCloud().add(series_name=\"差评文本\",\n",
    "                 data_pair=negative_words_wordcloud, #传入绘制词云图的数据\n",
    "                 word_size_range=[16, 80]) #word_size_range字号大小取值范围\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(\n",
    "        title=\"差评词云图\", \n",
    "        title_textstyle_opts=opts.TextStyleOpts(font_size=23) # 设置标题字号\n",
    " ),\n",
    "tooltip_opts=opts.TooltipOpts(is_show=True),# 设置为True 鼠标滑过文字会弹出提示框\n",
    "            ).\n",
    " render('neg_wordcloud.html')\n",
    ")"
   ],
   "id": "b4af23faefccedcc",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\数据分析\\\\day08\\\\02_代码\\\\neg_wordcloud.html'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 31
  }
 ],
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